This tidy data set contains 1,599 red wines with 11 variables on the chemical properties of the wine. At least 3 wine experts rated the quality of each wine, providing a rating between 0 (very bad) and 10 (very excellent). This analysis includes univariate, bivariate, and multivariate analysis around which chemical properties influence the quality of red wines.
## 'data.frame': 1599 obs. of 13 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...There are 1599 observations of 13 numeric variables. X appears to be the index.
## X fixed.acidity volatile.acidity citric.acid
## Min. : 1.0 Min. : 4.60 Min. :0.1200 Min. :0.000
## 1st Qu.: 400.5 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090
## Median : 800.0 Median : 7.90 Median :0.5200 Median :0.260
## Mean : 800.0 Mean : 8.32 Mean :0.5278 Mean :0.271
## 3rd Qu.:1199.5 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420
## Max. :1599.0 Max. :15.90 Max. :1.5800 Max. :1.000
## residual.sugar chlorides free.sulfur.dioxide
## Min. : 0.900 Min. :0.01200 Min. : 1.00
## 1st Qu.: 1.900 1st Qu.:0.07000 1st Qu.: 7.00
## Median : 2.200 Median :0.07900 Median :14.00
## Mean : 2.539 Mean :0.08747 Mean :15.87
## 3rd Qu.: 2.600 3rd Qu.:0.09000 3rd Qu.:21.00
## Max. :15.500 Max. :0.61100 Max. :72.00
## total.sulfur.dioxide density pH sulphates
## Min. : 6.00 Min. :0.9901 Min. :2.740 Min. :0.3300
## 1st Qu.: 22.00 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500
## Median : 38.00 Median :0.9968 Median :3.310 Median :0.6200
## Mean : 46.47 Mean :0.9967 Mean :3.311 Mean :0.6581
## 3rd Qu.: 62.00 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300
## Max. :289.00 Max. :1.0037 Max. :4.010 Max. :2.0000
## alcohol quality
## Min. : 8.40 Min. :3.000
## 1st Qu.: 9.50 1st Qu.:5.000
## Median :10.20 Median :6.000
## Mean :10.42 Mean :5.636
## 3rd Qu.:11.10 3rd Qu.:6.000
## Max. :14.90 Max. :8.000quality is an ordered, categorical, discrete variable. The values ranged only from 3 to 8, with a mean of 5.6 and median of 6. All other variables seem to be continuous quantities. fixed.acidity and volatile.acidity, also free.sulfur.dioxide and total.sulfur.dioxide may possible be dependent or subsets of each other.
## [1] "X" "fixed.acidity" "volatile.acidity"
## [4] "citric.acid" "residual.sugar" "chlorides"
## [7] "free.sulfur.dioxide" "total.sulfur.dioxide" "density"
## [10] "pH" "sulphates" "alcohol"
## [13] "quality"## X fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 1 7.4 0.70 0.00 1.9 0.076
## 2 2 7.8 0.88 0.00 2.6 0.098
## 3 3 7.8 0.76 0.04 2.3 0.092
## 4 4 11.2 0.28 0.56 1.9 0.075
## 5 5 7.4 0.70 0.00 1.9 0.076
## 6 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5
Quality ranges from 3 to 8, with a mean between 5 and 6.
Translate quality into categorial variable to look at relationship between other variables by quality grades for the analysis. Change type of the quality variable to factor and add it to the dataframe as a new variable quality.factor. In addition, 3 categories of quality was created- good (>= 7), bad (<=4), and medium (5 and 6).
The distribution of fixed acidity is right skewed. The median is around 8 with high concentration of wines with Fixed Acidity but due to some outliers.
The distribution of volatile acidity has two peaks around 0.4 and 0.6.
When plotted on a base 10 logarithmic scale, volatile.acidity appear to be normally-distributed.
Apart from some outliers, the distribution of citric acid looks almost rectangular.
The distribution of Residual Sugar is how much sugar is left after fermentation, positively skewed with high peaks at around 2.3 with many outliers present at the higher ranges, median is around 2.3 with many outliers present at the higher ranges, 1st and 3rd quartile are within 1.9 and 2.6.
pH has a normal distribution with most of the pH values between 3.2 and 3.4.
Sulphates centers at 0.6, right skew all the way to 2.0
Alcohol also follows a skewed distribution but here the skewness is less than that of Chlorides or Residual Sugars.
Normal: Quality, pH
Positively Skewed: Fixed acidity, Citric acid, Sulphates, Alcohol
Long Tail: Residual sugar
Bimodal: Volatile acidity
Increasing quality of wine when chemicals levels are higher 
The boxplots above show all the cases when wine quality increases with increasing values of another variables.
Cases when wine quality decreases while the values of variables increase.
The highest positive correlation is between density and fixed.acidity, as well as between fixed.acidity.
Previous boxplots show increasing levels of both sulphates and alcohol increase with higher quality of red wine. The scatter plot above shows a combination of sulphates and alcohol to classify and distinguish wine quality levels. The plot reveals a clear pattern, showing most of green and yellow dots (high-quality) in the place where both alcohol and sulphates level are high. There is also a visible range of violet dots in the middle of the plot, and orange dots(low-quality) in the bottom-left corner. This implies that such a combination of variables distinguish between different levels of wines.
Although the plot is not very clear, it reveals some patterns. It is visible here that the majority of red, orange, and yellow dots (low quality) are concentrated in the lower left, while the majority of blue dots (high quality) are concentrated in bottom half of the plot. Lower quality wine seems to have higher density and lower fixed acidity where higher quality wine have lower fixed acidity and lower density.
Influence of pH and sulfurdioxide on a quality of red wine, the left plot shows more high quality wines with lower pH and the opposite with low quality wine. The right plot shows high quality wines have higher free sulfur dioxide and totole sulfur dioxide and the opposite with low quality wine.
Quality, pH, density are normally distributed. Fixed acidity, citric acid, total sulfur dioxide, free sulfur dioxide, sulphates, alcohol are positively Skewed Residual sugar, chlorides have long tail distribution Volatile acidity has bimodal distribution.
The correlation are negative between pH and densiy, pH and fixed acidity. Density and fixed acidity are lower when pH is higher.
Although the plot is not very clear, it reveals some patterns. It is visible here that the majority of red, orange, and yellow dots (low quality) are concentrated in the lower left, while the majority of blue dots (high quality) are concentrated in bottom half of the plot. Lower quality wine seems to have higher density and lower fixed acidity where higher quality wine have lower fixed acidity and lower density.
In this exercise, my main struggle was to make various plots and get as few errors as possible. This requires lots of searching on Google, reviewing the lectures, and other research on related documents. Once I started to plot the data, the challenge was to have good understanding of each variable and to come up with which 2 or more variables should be included in the next analysis.
I feel more confident using R Studio after this exericise and I’m looking forward to do more analysis on R in the future. In future analysis, I’d like to explore more on doing corrilation and prediction after the EDA phase.